Trends Prediction Using Social Diffusion Models
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Pentland_Trends prediction.pdf
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151.07 KB
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Author(s)
Altshuler, Yaniv
Pan, Wei
Pentland, Alex Paul
Date Issued
2012
Journal
Social Computing, Behavioral - Cultural Modeling and Prediction
Publisher
Springer-Verlag
Citation
Altshuler, Yaniv, Wei Pan, and Alex Pentland. Trends Prediction Using Social Diffusion Models. In Social Computing, Behavioral - Cultural Modeling and Prediction. S.J. Yang, A.M. Greenberg, and M. Endsley (Eds.) Springer-Verlag, pp. 97–104, 2012. (Lecture notes in computer science ; 7227)
Version
Author's final manuscript
Abstract
The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday’s life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become “trends”. In this work we present an analytic model for the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community’s members. We present an analytic lower bound for the probability that emerging trends would successfully spread through the network. We demonstrate our model using two comprehensive social datasets — the Friends and Family experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the eToro social trading community.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/978-3-642-29047-3_12
